SoroushVahidi/scidocs huggingface dataset:
Ambulance redeployment: An approximate dynamic programming approach
(2009)

Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy and use this model in an approximate dynamic programming (ADP) context to compute high-quality redeployment policies. We find that the resulting ADP policies perform much better than sub-optimal static policies and marginally better than near-optimal static policies. Representative computational results for Edmonton, Alberta are included.
Design of household appliances for a Dc-based nanogrid system: An induction heating cooktop study case
(2013)

Efficient energy management is becoming an important issue when designing any electrical system. Recently, a significant effort has been devoted to the design of optimized micro and nanogrids comprising residential area subsystems. One of these approaches consists on the design of a dc-based nanogrid optimized for the interoperation of electric loads, sources, and storage elements. Home appliances are one of the main loads in such dc-based nanogrids. In this paper, the design and optimization of an appliance for operation in a dc-based nanogrid is detailed. An induction heating cooktop appliance will be considered as a reference example, being some of the design considerations generalizable to other appliances. The main design aspects, including the inductor system, power converter, EMC filter, and control are considered. Finally, some simulation results of the expected converter performance are shown.
PCS/W-CDMA dual-band MMIC power amplifier with a newly proposed linearizing bias circuit
(2002)

A personal communications service/wide-band code division multiple access (PCS/W-CDMA) dual-band monolithic microwave integrated circuit (MMIC) power amplifier with a single-chip MMIC and a single-path output matching network is demonstrated by adopting a newly proposed on-chip linearizer. The linearizer is composed of the base–emitter diode of an active bias transistor and a capacitor to provide an RF short at the base node of the active bias transistor. The linearizer enhances the linearity of the power amplifier effectively for both PCS and W-CDMA bands with no additional dc power consumption, and has negligible insertion power loss with almost no increase in die area. It improves the input 1-dB gain compression point by 18.5 (20) dB and phase distortion by 6.1 (12.42 ) at an output power of 28 (28) dBm for the PCS (W-CDMA) band while keeping the base bias voltage of the power amplifier as designed. A PCS and W-CDMA dual-band InGaP heterojunction bipolar transistor MMIC power amplifier with single input and output and no switch for band selection is embodied by implementing the linearizer and by designing the amplifier to have broad-band characteristics. The dual-band power amplifier exhibits an output power of 30 (28.5) dBm, power-added efficiency of 39.5% (36%), and adjacent channel power ratio of 46 ( 50) dBc at the output power of 28 (28) dBm under 3.4-V operation voltage for PCS (W-CDMA) applications.
Punica granatum (pomegranate) and its potential for prevention and treatment of inflammation and cancer.
(2007)

The last 7 years have seen over seven times as many publications indexed by Medline dealing with pomegranate and Punica granatum than in all the years preceding them. Because of this, and the virtual explosion of interest in pomegranate as a medicinal and nutritional product that has followed, this review is accordingly launched. The pomegranate tree, Punica granatum, especially its fruit, possesses a vast ethnomedical history and represents a phytochemical reservoir of heuristic medicinal value. The tree/fruit can be divided into several anatomical compartments: (1) seed, (2) juice, (3) peel, (4) leaf, (5) flower, (6) bark, and (7) roots, each of which has interesting pharmacologic activity. Juice and peels, for example, possess potent antioxidant properties, while juice, peel and oil are all weakly estrogenic and heuristically of interest for the treatment of menopausal symptoms and sequellae. The use of juice, peel and oil have also been shown to possess anticancer activities, including interference with tumor cell proliferation, cell cycle, invasion and angiogenesis. These may be associated with plant based anti-inflammatory effects, The phytochemistry and pharmacological actions of all Punica granatum components suggest a wide range of clinical applications for the treatment and prevention of cancer, as well as other diseases where chronic inflammation is believed to play an essential etiologic role.
The development of a new test of agility for rugby league.
(2010)

Agility requires change of direction speed (CODS) and also perceptual and decision-making skills and reaction speed. The purpose of this study was to develop a reliable and valid agility test for rugby league, which stressed all those dimensions. Players from a subelite rugby league team were tested twice on a sport-specific reactive agility test (RAT) and CODS test. Data were analyzed for reliability. For validity results from the subelite groups, first test was compared with data from an elite group. The RAT required participants to run toward an unpredictable life-size video of an attacking opponent and react to that video by changing direction. The CODS test required the same movement patterns however direction changes were preplanned. The subelite group's mean time to complete the CODS test and RAT on their first test was 1.67 ± 0.15 and 1.98 ± 0.16 seconds, respectively, and 1.62 ± 0.14 and 1.91 ± 0.17 seconds, respectively, on their second test (results are ± σ). Statistical analyses revealed no significant difference in means (p < 0.05) and good correlation (intraclass correlation coefficient = 0.87 and 0.82, respectively). The elite group's mean time to complete the tests was 1.65 ± 0.09 and 1.79 ±0.12 seconds, respectively. Statistical analyses revealed a significant difference in mean RAT time between the elite group and the subelite group (p < 0.05). The RAT was reliable and valid. Performance differences on the RAT were attributed to differences in perceptual skills and/or reaction ability. Testing and training agility should therefore stress those dimensions of agility and not just CODS.
Diabetes Prediction by using Bacterial Foraging Optimization Algorithm And Artificial Neural Network
(2016)

Diagnosis of any diseases earlier is always preferable .diabetes is one such diseases.it reaches to the epidemic proportional in many developing countries and also reaching to industrialized nations. In this study, we investigate a programmable method to analyze diabetes diseases based on BFO and ANN.ANN is a powerful tool and solves the problems such as classification and prediction. Its performance is highly dependent on its architecture and weights. To gain high efficiency in ANN, an appropriate Architecture and learning Algorithm is preferable. The experiments on dataset occupied from undertaking repository to verify the potential of BFO algorithm and showed that BFO-ANN is more accurate than traditional ANN in terms of classification. General Terms-Artificial Neural Network, BacterialForaging Optimization Algorithm.
Cross-Domain Ranking via Latent Space Learning
(2017)

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.
Mitigation of Continuous and Pulsed Radio Interference with GNSS Antenna Arrays
(2008)

Dr. Andriy Konovaltsev received his engineer diploma and the Ph.D. degree in electrical engineering from Kharkov State Technical University of Radio Electronics, Ukraine in 1993 and 1996, correspondingly. He joined the Institute of Communications and Navigation of DLR in 2001. His research interests are in array processing for satellite navigation systems, signal processing algorithms for navigation receivers including synchronisation, multipath and radio interference mitigation.
Facial Expression Recognition in Older Adults using Deep Machine Learning
(2017)

Facial Expression Recognition is still one of the challenging fields in pattern recognition and machine learning science. Despite efforts made in developing various methods for this topic, existing approaches lack generalizability and almost all studies focus on more traditional hand-crafted features extraction to characterize facial expressions. Moreover, effective classifiers to model the spatial and temporary patterns embedded in facial expressions ignore the effects of facial attributes, such as age, on expression recognition even though research indicates that facial expression manifestation varies with ages. Although there are large amount of benchmark datasets available for the recognition of facial expressions, only few datasets contains faces of older adults. Consequently the current scientific literature has not exhausted this topic. Recently, deep learning methods have been attracting more and more researchers due to their great success in various computer vision tasks, mainly because they avoid a process of feature definition and extraction which is often very difficult due to the wide variability of the facial expressions. Based on the deep learning theory, a neural network for facial expression recognition in older adults is constructed by combining a Stacked Denoising Auto-Encoder method to pre-train the network and a supervised training that provides a fine-tuning adjustment of the network. For the supervised classification layer, the -class softmax classifier was implemented, where is the number of expressions to be recognized. The performance are evaluated on two benchmark datasets (FACES and Lifespan), that are the only ones that contain facial expressions of the elderly. The achieved results show the superiority of the proposed deep learning approach compared to the conventional non-deep learning based facial expression recognition methods used in this context.
Wasserstein Propagation for Semi-Supervised Learning
(2014)

Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semisupervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application; in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.
Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy
(2014)

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS andWDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem. 2013 Elsevier Ltd. All rights reserved.
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
(2015)

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) handcrafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a loglinear classifier with hand-crafted features gives state-of-the-art results. We made our implementation available for general use1.
Exploring Semantic Properties of Sentence Embeddings
(2018)

Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.
High Gain Wide-Band U-Shaped Patch Antennas With Modified Ground Planes
(2013)

A high gain wideband U-shaped patch antenna with two equal arms on poly tetra fluoro ethylene (PTFE) substrate is presented. An inverted U-shaped slot is introduced on the circular or square shaped ground plane just under the U-shaped patch. In this communication the effect of size and shape of the ground plane on impedance bandwidth is studied. Maximum impedance bandwidth of 86.79% (4.5–11.4 GHz) is obtained with circular shaped ground plane with diameter 36 mm. The highest gain achieved is 4.1 dBi. The simulated results are confirmed experimentally. The proposed antenna is simple in structure compared to the regular stacked or coplanar parasitic patch antennas. It is highly suitable for wireless communications.
Influence and correlation in social networks
(2008)

In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.
 This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.
 In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.
 We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.
Simulation on Simulink AC4 model (200hp DTC induction motor drive) using Fuzzy Logic controller
(2010)

Classical direct torque control (DTC) has advantage in absence of coordinate transform and voltage modulator block. However, it may have disadvantage in controlling electromagnetic torque. DTC produces high ripple in electromagnetic torque as it is not directly controlled. High torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. Thus, Fuzzy Logic controller is applied to reduce electromagnetic torque ripple. This paper presents the simulation analysis of Fuzzy Logic Direct Torque Controller (FLDTC) of induction machine drives. However, only the design of the controller using built in DTC induction motor drive model of Simulink AC4 will be discussed in this paper. Using FLDTC, the resulting electromagnetic torque from produced less ripple than classical DTC.
Camera calibration with spheres: linear approaches
(2005)

This paper addresses the problem of camera calibration from spheres. By studying the relationship between the dual images of spheres and that of the absolute conic, a linear solution has been derived from a recently proposed non-linear semi-definite approach. However, experiments show that this approach is quite sensitive to noise. In order to overcome this problem, a second approach has been proposed, where the orthogonal calibration relationship is obtained by regarding any two spheres as a surface of revolution. This allows a camera to be fully calibrated from an image of three spheres. Besides, a conic homography is derived from the imaged spheres, and from its eigenvectors the orthogonal invariants can be computed directly. Experiments on synthetic and real data show the practicality of such an approach.
Smart music player integrating facial emotion recognition and music mood recommendation
(2017)

Songs, as a medium of expression, have always been a popular choice to depict and understand human emotions. Reliable emotion based classification systems can go a long way in helping us parse their meaning. However, research in the field of emotion-based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user's face as an input and makes use of deep learning algorithms to identify their mood with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood classes. The Recommendation Module suggests songs to the user by mapping their emotions to the mood type of the song, taking into consideration the preferences of the user.
Jubatus : An Open Source Platform for Distributed Online Machine Learning
(2013)

Distributed computing is essential for handling very large datasets. Online learning is also promising for learning from rapid data streams. However, it is still an unresolved problem how to combine them for scalable learning and prediction on big data streams. We propose a general computational framework called loose model sharing for online and distributed machine learning. The key is to share only models rather than data between distributed servers. We also introduce Jubatus, an open source software platform based on the framework. Finally, we describe the details of implementing classifier and nearest neighbor algorithms, and discuss our experimental evaluations.
Reinforcement Learning Testbed for Power-Consumption Optimization
(2018)

Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and often lead to suboptimal or unstable performance. In this paper, we show how deep reinforcement learning techniques can be used to control the cooling system of a simulated data center. In contrast to common control algorithms, those based on reinforcement learning techniques can optimize a system’s performance automatically without the need of explicit model knowledge. Instead, only a reward signal needs to be designed. We evaluated the proposed algorithm on the open source simulation platform EnergyPlus. The experimental results indicate that we can achieve 22% improvement compared to a model-based control algorithm built into the EnergyPlus. To encourage the reproduction of our work as well as future research, we have also publicly released an open-source EnergyPlus wrapper interface 1 directly compatible with existing reinforcement learning frameworks.
Mermaid: Integrating Vertex-Centric with Edge-Centric for Real-World Graph Processing
(2017)

There has been increasing interests in processing large-scale real-world graphs, and recently many graph systems have been proposed. Vertex-centric GAS (Gather-Apply-Scatter) and Edge-centric GAS are two graph computation models being widely adopted, and existing graph analytics systems commonly follow only one computation model, which is not the best choice for real-world graph processing. In fact, vertex degrees in real-world graphs often obey skewed power-law distributions: most vertices have relatively few neighbors while a few have many neighbors. We observe that vertex-centric GAS for high-degree vertices and edge-centric GAS for low-degree vertices is a much better choice for real-world graph processing. In this paper, we present Mermaid, a system for processing large-scale real-world graphs on a single machine. Mermaid skillfully integrates vertex-centric GAS with edge-centric GAS through a novel vertex-mapping mechanism, and supports streamlined graph processing. On a total of 6 practical natural graph processing tasks, we demonstrate that, on average, Mermaid achieves 1.83x better performance than the state-of-the-art graph system on a single machine.